The Clustering Of Sapodilla Fruit (Manilkara Zapota) Maturity Levels Based On Color Using K-Means Clustering Method: Clustering Tingkat Kematangan Buah Sapodilla (Manilkara Zapota) Berdasarkan Warna Menggunakan Metode K-Means Clustering - Bayu Sumadika, Wayan; Syahid Pebriadi , Muhammad (INSTALL: Information System and Technology Journal, 2024)
The study aimed to evaluate the accuracy of determining sapodilla fruit maturity using the K-Means Clustering algorithm, a method that partitions data into clusters of similar characteristics; by applying K-Means Clustering on data samples obtained from Kaggle, and using Matlab for pixel value calculation, the algorithm effectively classified 150 sapodilla fruit samples into two clusters—75 mature and 75 raw—with an accuracy of 92.2% for mature sapodilla and 94.5% for raw sapodilla, demonstrating that K-Means Clustering, a straightforward and user-friendly algorithm, is highly effective in distinguishing sapodilla fruit maturity levels.